论文标题
边界面：带有噪声标签自校正面部识别的采矿框架
BoundaryFace: A mining framework with noise label self-correction for Face Recognition
论文作者
论文摘要
由于损失功能的进步和训练集规模的爆炸性增长，近年来，面部识别取得了巨大进展。正确设计的损失被视为提取分类的区分特征的关键。已经提出了几种基于保证金的损失，作为面部识别中软性损失的替代方法。但是，仍有两个问题要考虑：1）他们忽略了硬采矿对判别学习的重要性。 2）在大规模数据集中存在普遍存在的标签噪声，这可能会严重损害模型的性能。在本文中，从决策边界的角度开始，我们提出了一个新颖的采矿框架，该框架重点介绍了样本的地面真相类中心与其最近的负面阶级中心之间的关系。具体而言，提出了一个封闭式噪声标签自我纠正模块，使该框架在包含大量标签噪声的数据集上正常运行。所提出的方法在各种面部识别基准中始终优于SOTA方法。培训代码已在https://github.com/swjtu-3dvision/boundaryface上发布。
Face recognition has made tremendous progress in recent years due to the advances in loss functions and the explosive growth in training sets size. A properly designed loss is seen as key to extract discriminative features for classification. Several margin-based losses have been proposed as alternatives of softmax loss in face recognition. However, two issues remain to consider: 1) They overlook the importance of hard sample mining for discriminative learning. 2) Label noise ubiquitously exists in large-scale datasets, which can seriously damage the model's performance. In this paper, starting from the perspective of decision boundary, we propose a novel mining framework that focuses on the relationship between a sample's ground truth class center and its nearest negative class center. Specifically, a closed-set noise label self-correction module is put forward, making this framework work well on datasets containing a lot of label noise. The proposed method consistently outperforms SOTA methods in various face recognition benchmarks. Training code has been released at https://github.com/SWJTU-3DVision/BoundaryFace.
